Two partitional methods for interval-valued data using Mahalanobis distances

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3315, Issue , 2004, Pages 454-463

  De Souza, Renata M C R ^a   De Carvalho, Francisco A T ^a   Tenorio, Camilo P ^a  

^a FEDERAL UNIVERSITY OF PERNAMBUCO   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE SYSTEMS; ALGORITHMS; DATA MINING; FUNCTIONS; IMAGE PROCESSING; MATHEMATICAL MODELS; MONTE CARLO METHODS; OPTIMIZATION; PATTERN RECOGNITION; VECTORS; NORMAL DISTRIBUTION;

INTERVAL-VALUED DATA; MAHALANOBIS DISTANCES; NORMAL DISTRIBUTIONS; TWO PARTITIONAL METHODS; ADAPTIVE METHODS; ADAPTIVE VERSIONS; ADEQUACY CRITERION; CLUSTER QUALITIES; QUANTITATIVE DATA SETS;

DATA REDUCTION; ARTIFICIAL INTELLIGENCE;

EID: 22944492394     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-30498-2_45     Document Type: Conference Paper

References (10)

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